Your search found 3 records
1 Miranda, F. R.; Gondim, R. S.; Costa, C.A.G. 2006. Evapotranspiration and crop coefficients for tabasco pepper (Capsicum frutescens L.) Agricultural Water Management, 82(1/2):237-246.
(Location: IWMI-HQ Call no: PER Record No: H038688)
2 Torres, A. B. B.; da Rocha, A. R.; Coelho da Silva, T. L.; de Souza, J. N.; Gondim, R. S.. 2020. Multilevel data fusion for the internet of things in smart agriculture. Computers and Electronics in Agriculture, 171:105309. [doi: https://doi.org/10.1016/j.compag.2020.105309]
Decision support systems ; Internet ; Agriculture ; Irrigation ; Soil moisture ; Evapotranspiration ; Energy consumption ; Linear models ; Sensors ; Crops ; Cashews ; Coconuts / Brazil / Paraipaba
(Location: IWMI HQ Call no: e-copy only Record No: H049724)
(7.91 MB)
The Internet of Things (IoT) aims to enable objects to sense, identify, and analyze the world, but to achieve such goal cost-effectively, it should involve low-cost solutions. That implies a series of limitations, such as small battery life, limited storage capabilities, low accuracy, and imprecise sensors. Data fusion is one of the most widely used methods for improving sensor accuracy and providing a more precise decision. Therefore, we propose Hydra, a multilevel data fusion architecture, to improve sensor accuracy, identify application target events, and make more accurate decisions. Hydra is composed of three layers: low-level (sensor data fusion), medium-level (events and decision making), and high-level (decision fusion based on multiple applications). In partnership with Embrapa (Brazilian Agricultural Research Corporation), we instantiated Hydra for the smart agriculture domain, and we also developed two applications aiming smart water management. The first application goal was to determine the need for irrigation based on soil moisture levels, and the second ascertained the adequate irrigation time by estimating the crop’s evapotranspiration (rate of water evaporation by the soil and transpiration by plants). We performed a set of experiments to assess Hydra: (i) evaluation of methods to detect and remove outliers; (ii) analyze data resulting from the applications; (iii) the use of machine learning to create a new accurate evapotranspiration model based on the sensors data. The results indicate that a combination of the ESD method (Extreme Studentized Deviate) and WRKF filter (Weighted Outlier-Robust Kalman Filter) was the best method to identify and remove outliers. Moreover, we generated an evapotranspiration model using the SVM (Support Machine Vector) quadratic machine-learning model that produced values close to the evapotranspiration reference model (Penman-Monteith).
3 Walker, D. W.; Oliveira, J. L.; Cavalcante, L.; Kchouk, S.; Neto, G. R.; Melsen, L. A.; Fernandes, F. B.; Mitroi, V.; Gondim, R. S.; Martins, E. S. P. R.; van Oel, P. R. 2024. It's not all about drought: What “drought impacts” monitoring can reveal. International Journal of Disaster Risk Reduction, 103:104338. [doi: https://doi.org/10.1016/j.ijdrr.2024.104338]
Drought ; Monitoring ; Vulnerability ; Risk reduction ; Mitigation ; Infrastructure ; Hydrometeorology ; Crop losses ; Socioeconomic aspects ; Water resources ; Rainfall ; Water supply / Brazil
(Location: IWMI HQ Call no: e-copy only Record No: H052730)
https://www.sciencedirect.com/science/article/pii/S2212420924001006/pdfft?md5=7cf44ae1a35e680dcbaa259211242a6f&pid=1-s2.0-S2212420924001006-main.pdf
https://vlibrary.iwmi.org/pdf/H052730.pdf
https://vlibrary.iwmi.org/pdf/H052730.pdf
(5.06 MB) (5.06 MB)
Drought impacts monitoring has been called the missing piece in drought assessment. The potential to improve drought management is high but uncertain due to rare analyses of impacts datasets, predominantly because there are few impacts monitoring programmes to generate the datasets. Drought impacts monitoring is conducted on the ground in much of Brazil by local observers at monthly and municipality scale to support the Brazilian Drought Monitor. In Ceará state, within drought-prone semiarid northeast Brazil, over 3600 drought impacts reports were completed by agricultural extension officers from 2019 to 2022. We investigated, through manual coding and observer interviews, the reported drought impacts and impact drivers. Analysis provided a catalogue of the experienced impacts and showed that impacts still occur, and are often normalised, during non-drought periods, sometimes as lingering effects of previous droughts. The impact drivers were predominantly non-extreme hydrometeorological conditions or a result of socio-technical vulnerabilities such as insufficient water infrastructure. The normalisation of “impacts” included, in particular: a generally accepted high level of crop losses and consistently low reservoir levels around which the agricultural and domestic systems are adapted. Conventional drought indices often did not align with experienced impact severity, highlighting the limitations of relying solely on these indices for emergency response. Continual impacts monitoring could be extremely valuable anywhere in the world for identifying vulnerabilities and informing proactive measures to reduce drought and other hazard risk, in addition to guiding targeted mitigation efforts.
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